OpenRaiser/Intern-Atlas huggingface dataset:
Can't you hear me knocking: Identification of user actions on Android apps via traffic analysis
Venue: arXiv cs.CR 2014
Year: 2014

While smartphone usage become more and more pervasive, people start also asking to which extent such devices can be maliciously exploited as "tracking devices". The concern is not only related to an adversary taking physical or remote control of the device (e.g., via a malicious app), but also to what a passive adversary (without the above capabilities) can observe from the device communications. Work in this latter direction aimed, for example, at inferring the apps a user has installed on his device, or identifying the presence of a specific user within a network. In this paper, we move a step forward: we investigate to which extent it is feasible to identify the specific actions that a user is doing on his mobile device, by simply eavesdropping the device's network traffic. In particular, we aim at identifying actions like browsing someone's profile on a social network, posting a message on a friend's wall, or sending an email. We design a system that achieves this goal starting from encrypted TCP/IP packets: it works through identification of network flows and application of machine learning techniques. We did a complete implementation of this system and run a thorough set of experiments, which show that it can achieve accuracy and precision higher than 95%, for most of the considered actions.
Strategic Port Graph Rewriting: An Interactive Modelling and Analysis Framework
Venue: arXiv cs.LO 2014
Year: 2014

We present strategic portgraph rewriting as a basis for the implementation of visual modelling and analysis tools. The goal is to facilitate the specification, analysis and simulation of complex systems, using port graphs. A system is represented by an initial graph and a collection of graph rewriting rules, together with a user-defined strategy to control the application of rules. The strategy language includes constructs to deal with graph traversal and management of rewriting positions in the graph. We give a small-step operational semantics for the language, and describe its implementation in the graph transformation and visualisation tool PORGY.
Multi-agents Architecture for Semantic Retrieving Video in Distributed Environment
Venue: arXiv cs.IR 2014
Year: 2014

This paper presents an integrated multi-agents architecture for indexing and retrieving video information.The focus of our work is to elaborate an extensible approach that gathers a priori almost of the mandatory tools which palliate to the major intertwining problems raised in the whole process of the video lifecycle (classification, indexing and retrieval). In fact, effective and optimal retrieval video information needs a collaborative approach based on multimodal aspects. Clearly, it must to take into account the distributed aspect of the data sources, the adaptation of the contents, semantic annotation, personalized request and active feedback which constitute the backbone of a vigorous system which improve its performances in a smart way
Error Correction for Physical Unclonable Functions Using Generalized Concatenated Codes
Venue: arXiv cs.IT 2014
Year: 2014

Physical Unclonable Functions can be used for secure key generation in cryptographic applications. It is explained how methods from coding theory must be applied in order to ensure reliable key regeneration. Based on previous work, we show ways how to obtain better results with respect to error probability and codeword length. Also, an example based on Generalized Concatenated codes is given, which improves upon used coding schemes for PUFs.
Enhancing $\ell_1$-minimization estimates of polynomial chaos expansions using basis selection
Venue: arXiv cs.NA 2014
Year: 2014

In this paper we present a basis selection method that can be used with $\ell_1$-minimization to adaptively determine the large coefficients of polynomial chaos expansions (PCE). The adaptive construction produces anisotropic basis sets that have more terms in important dimensions and limits the number of unimportant terms that increase mutual coherence and thus degrade the performance of $\ell_1$-minimization. The important features and the accuracy of basis selection are demonstrated with a number of numerical examples. Specifically, we show that for a given computational budget, basis selection produces a more accurate PCE than would be obtained if the basis is fixed a priori. We also demonstrate that basis selection can be applied with non-uniform random variables and can leverage gradient information.
Fence patrolling by mobile agents with distinct speeds
Venue: arXiv cs.CG 2014
Year: 2014

Suppose we want to patrol a fence (line segment) using k mobile agents with given speeds v_1, ..., v_k so that every point on the fence is visited by an agent at least once in every unit time period. Czyzowicz et al. conjectured that the maximum length of the fence that can be patrolled is (v_1 + ... + v_k)/2, which is achieved by the simple strategy where each agent i moves back and forth in a segment of length v_i/2. We disprove this conjecture by a counterexample involving k = 6 agents. We also show that the conjecture is true for k = 2, 3.
Two-pass Discourse Segmentation with Pairing and Global Features
Venue: arXiv cs.CL 2014
Year: 2014

Previous attempts at RST-style discourse segmentation typically adopt features centered on a single token to predict whether to insert a boundary before that token. In contrast, we develop a discourse segmenter utilizing a set of pairing features, which are centered on a pair of adjacent tokens in the sentence, by equally taking into account the information from both tokens. Moreover, we propose a novel set of global features, which encode characteristics of the segmentation as a whole, once we have an initial segmentation. We show that both the pairing and global features are useful on their own, and their combination achieved an $F_1$ of 92.6% of identifying in-sentence discourse boundaries, which is a 17.8% error-rate reduction over the state-of-the-art performance, approaching 95% of human performance. In addition, similar improvement is observed across different classification frameworks.
A low-latency control plane for dense cellular networks
Venue: arXiv cs.NI 2014
Year: 2014

In order to keep up with the increasing demands for capacity, cellular networks are becoming increasingly dense and heterogeneous. Dense deployments are expected to provide a linear capacity scaling with the number of small cells deployed due to spatial reuse gains. However in practice network capacity is severely limited in dense networks due to interference. The primary reason is that the current LTE control plane deployment model has very high latency and is unable to cope with the demand of implementing interference management techniques that require coordination on a millisecond timeframe. This paper presents SwiftC, a novel low-latency control plane design for LTE networks. SwiftC's novel contribution is a design for efficiently sending and receiving control plane messages over the LTE spectrum itself, thus creating a direct and low-latency coordination signaling link between small cells and the macrocell. SwiftC builds on recent work in full duplex radios and shows via prototype implementations that a low latency control plane can be built over the existing LTE network without wasting licensed spectrum. We also show the benefits of SwiftC in implementing complex interference management techniques, and show that with SwiftC small cell deployments can achieve almost a linear capacity scaling with every small cell deployed.
Design of Quantum Stabilizer Codes From Quadratic Residues Sets
Venue: arXiv cs.IT 2014
Year: 2014

We propose two types, namely Type-I and Type-II, quantum stabilizer codes using quadratic residue sets of prime modulus given by the form $p=4n\pm1$. The proposed Type-I stabilizer codes are of cyclic structure and code length $N=p$. They are constructed based on multi-weight circulant matrix generated from idempotent polynomial, which is obtained from a quadratic residue set. The proposed Type-II stabilizer codes are of quasi-cyclic (QC) structure and code length $N=pk$, where $k$ is the size of a quadratic residue set. They are constructed based on structured sparse-graphs codes derived from proto-matrix and circulant permutation matrix. With the proposed methods, we design rich classes of cyclic and quasi-cyclic quantum stabilizer codes with variable code length. We show how the commutative constraint (also referred to as the Symplectic Inner Product constraint) for quantum codes can be satisfied for each proposed construction method. We also analyze both the dimension and distance for Type-I stabilizer codes and the dimension of Type-II stabilizer codes. For the cyclic quantum stabilizer codes, we show that they meet the existing distance bounds in literature.
Opportunistic Routing Based on Daily Routines
Venue: arXiv cs.NI 2014
Year: 2014

Opportunistic routing is being investigated to enable the proliferation of low-cost wireless applications. A recent trend is looking at social structures, inferred from the social nature of human mobility, to bring messages close to a destination. To have a better picture of social structures, social-based opportunistic routing solutions should consider the dynamism of users' behavior resulting from their daily routines. We address this challenge by presenting dLife, a routing algorithm able to capture the dynamics of the network represented by time-evolving social ties between pair of nodes. Experimental results based on synthetic mobility models and real human traces show that dLife has better delivery probability, latency, and cost than proposals based on social structures.
Social-aware Opportunistic Routing: The New Trend
Venue: arXiv cs.NI 2014
Year: 2014

Since users move around based on social relationships and interests, the resulting movement patterns can represent how nodes are socially connected (i.e., nodes with strong social ties, nodes that meet occasionally by sharing the same working environment). This means that social interactions reflect personal relationships (e.g., family, friends, co-workers, passers-by) that may be translated into statistical contact opportunities within and between social groups over time. Such contact opportunities may be exploited to ensure good data dissemination and retrieval, even in the presence of intermittent connectivity. Thus, in the last years, a new trend based on social similarity emerged where social relationships, interests, popularity and among others, are used to improve opportunistic routing. In this chapter, the reader will learn about the different approaches related to opportunistic routing focusing on the social-aware approaches and how such approaches make use of social information derived from opportunistic contacts to improve data forwarding. Additionally, a brief overview on the existing taxonomies for opportunistic routing as well as an updated one are provided along with a set of experiments in scenarios based on synthetic mobility models and human traces in order to show the potential of social-aware solutions.
The 1-2-3-Toolkit for Building Your Own Balls-into-Bins Algorithm
Venue: arXiv cs.DC 2014
Year: 2014

In this work, we examine a generic class of simple distributed balls-into-bins algorithms. Exploiting the strong concentration bounds that apply to balls-into-bins games, we provide an iterative method to compute accurate estimates of the remaining balls and the load distribution after each round. Each algorithm is classified by (i) the load that bins accept in a given round, (ii) the number of messages each ball sends in a given round, and (iii) whether each such message is given a rank expressing the sender's inclination to commit to the receiving bin (if feasible). This novel ranking mechanism results in notable improvements, in particular in the number of balls that may commit to a bin in the first round of the algorithm. Simulations independently verify the correctness of the results and confirm that our approximation is highly accurate even for a moderate number of $10^6$ balls and bins.
A comparative study between seasonal wind speed by Fourier and Wavelet analysis
Venue: arXiv cs.CE 2014
Year: 2014

Wind Energy is a useful resource for Renewable energy purpose. Wind speed plays a vital role for wind energy calculation of certain location. So, it is very much necessary to know the wind speed data characteristics. In this paper fourier and wavelet transform are applied to study the wind speed data. We have compared wind speed of winter with summer by taking their speed into account using various discrete wavelets namely Haar and Daubechies-4 (Db-4). Also the periodicity of wind speed is checked using Continuous Wavelet Transform (MCWT) like Morlet. Thereafter a comparative study is done for detecting the periodicity of both summer and winter. Then wavelet coherence is checked between these two data for extracting the phase coherency information.
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
Venue: arXiv cs.CV 2014
Year: 2014

Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis. For anatomically high-variability abdominal organs such as the pancreas, previous segmentation works report low accuracies when comparing to organs like the heart or liver. In this paper, a fully-automated bottom-up method is presented for pancreas segmentation, using abdominal computed tomography (CT) scans. The method is based on a hierarchical two-tiered information propagation by classifying image patches. It labels superpixels as pancreas or not via pooling patch-level confidences on 2D CT slices over-segmented by the Simple Linear Iterative Clustering approach. A supervised random forest (RF) classifier is trained on the patch level and a two-level cascade of RFs is applied at the superpixel level, coupled with multi-channel feature extraction, respectively. On six-fold cross-validation using 80 patient CT volumes, we achieved 68.8% Dice coefficient and 57.2% Jaccard Index, comparable to or slightly better than published state-of-the-art methods.
Twitter User Classification using Ambient Metadata
Venue: arXiv cs.SI 2014
Year: 2014

Microblogging websites, especially Twitter have become an important means of communication, in today's time. Often these services have been found to be faster than conventional news services. With millions of users, a need was felt to classify users based on ambient metadata associated with their user accounts. We particularly look at the effectiveness of the profile description field in order to carry out the task of user classification. Our results show that such metadata can be an effective feature for any classification task.
RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization
Venue: arXiv cs.NI 2014
Year: 2014

RF sensor networks are used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfalls or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-of-the-art RTI methods.
QPSK Waveform for MIMO Radar with Spectrum Sharing Constraints
Venue: arXiv cs.NI 2014
Year: 2014

Multiple-input multiple-output (MIMO) radar is a relatively new concept in the field of radar signal processing. Many novel MIMO radar waveforms have been developed by considering various performance metrics and constraints. In this paper, we show that finite alphabet constant-envelope (FACE) quadrature-pulse shift keying (QPSK) waveforms can be designed to realize a given covariance matrix by transforming a constrained nonlinear optimization problem into an unconstrained nonlinear optimization problem. In addition, we design QPSK waveforms in a way that they don't cause interference to a cellular system, by steering nulls towards a selected base station (BS). The BS is selected according to our algorithm which guarantees minimum degradation in radar performance due to null space projection (NSP) of radar waveforms. We design QPSK waveforms with spectrum sharing constraints for a stationary and moving radar platform. We show that the waveform designed for stationary MIMO radar matches the desired beampattern closely, when the number of BS antennas $N^{\text{BS}}$ is considerably less than the number of radar antennas $M$, due to quasi-static interference channel. However, for moving radar the difference between designed and desired waveforms is larger than stationary radar, due to rapidly changing channel.
Beyond KernelBoost
Venue: arXiv cs.CV 2014
Year: 2014

In this Technical Report we propose a set of improvements with respect to the KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with a scheme inspired by Auto-Context, but that is suitable in situations where the lack of large training sets poses a potential problem of overfitting. The aim is to capture the interactions between neighboring image pixels to better regularize the boundaries of segmented regions. As in Auto-Context [Tu et al., PAMI 2009] the segmentation process is iterative and, at each iteration, the segmentation results for the previous iterations are taken into account in conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009], we organize our recursion so that the classifiers can progressively focus on difficult-to-classify locations. This lets us exploit the power of the decision-tree paradigm while avoiding over-fitting. In the context of this architecture, KernelBoost represents a powerful building block due to its ability to learn on the score maps coming from previous iterations. We first introduce two important mechanisms to empower the KernelBoost classifier, namely pooling and the clustering of positive samples based on the appearance of the corresponding ground-truth. These operations significantly contribute to increase the effectiveness of the system on biomedical images, where texture plays a major role in the recognition of the different image components. We then present some other techniques that can be easily integrated in the KernelBoost framework to further improve the accuracy of the final segmentation. We show extensive results on different medical image datasets, including some multi-label tasks, on which our method is shown to outperform state-of-the-art approaches. The resulting segmentations display high accuracy, neat contours, and reduced noise.
Improving the Scalability of DPWS-Based Networked Infrastructures
Venue: arXiv cs.DC 2014
Year: 2014

The Devices Profile for Web Services (DPWS) specification enables seamless discovery, configuration, and interoperability of networked devices in various settings, ranging from home automation and multimedia to manufacturing equipment and data centers. Unfortunately, the sheer simplicity of event notification mechanisms that makes it fit for resource-constrained devices, makes it hard to scale to large infrastructures with more stringent dependability requirements, ironically, where self-configuration would be most useful. In this report, we address this challenge with a proposal to integrate gossip-based dissemination in DPWS, thus maintaining compatibility with original assumptions of the specification, and avoiding a centralized configuration server or custom black-box middleware components. In detail, we show how our approach provides an evolutionary and non-intrusive solution to the scalability limitations of DPWS and experimentally evaluate it with an implementation based on the the Web Services for Devices (WS4D) Java Multi Edition DPWS Stack (JMEDS).
Learning From Ordered Sets and Applications in Collaborative Ranking
Venue: arXiv cs.LG 2014
Year: 2014

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed $5$ stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches $(N!/2)6.93145^{N+1}$ as $N$ approaches infinity. We propose a \texttt{split-and-merge} Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.
Speakable in quantum mechanics: babbling on
Venue: arXiv cs.LO 2014
Year: 2014

This paper consists of a short version of the derivation of the intuitionistic quantum logic L_QM (which was originally introduced by Caspers, Heunen, Landsman and Spitters). The elaboration consists of extending this logic to a classical logic CL_QM. Some first steps are then taken towards setting up a probabilistic framework based on CL_QM in terms of Rényi's conditional probability spaces. Comparisons are then made with the traditional framework for quantum probabilities.
Memetic Search in Differential Evolution Algorithm
Venue: arXiv cs.NE 2014
Year: 2014

Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.